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Monday, June 22, 2020

Elon's Estimates - Mistaking A Clear View For A Short Distance


Elon Musk is known for many things: Zip2, PayPal, SpaceX, Tesla, Boring Co...
But he is also known for his over-enthusiastic estimates of when a technology can be delivered. Other than Model Y, every one of Tesla's vehicles has been late to market. In December of 2015, Musk said that full self-driving would be available in 2 years; it has made progress, but it is still not here. And more recently on the Joe Rogan podcast, Musk predicted that within 5 to 10 years people will be able to directly communicate thoughts via brain implants rather than using the slow analog process of speech or writing.

For followers of Musk (fans and detractors alike), this is known as MST or Musk Standard Time. Converting from MST to a Gregorian calendar is not an easy task. It involves leap years and slide rules and it is not possible in all instances.

I don't point this out for ridicule; rather it is to ask the question: Why does Musk continue to make bold predictions on unrealistic timelines?

In short, I think that he is falling into the trap that Paul Saffo warned against:

       Never mistake a clear view for a short distance. ~Paul Saffo

Musk has a clear view of his plan. He's well aware that there will be challenges, but he has built teams and achieved many things that were deemed previously impossible. Create a door-to-door driving directions website - Check; Create an internet payment system - Check; Make sexy fast electric cars that blow away gas cars costing 10 times as much - Check; Create giant energy storage systems that change the way energy is bought and sold - Check; Land rockets on autonomous drones ships at sea - Check; Launch the largest network of low Earth orbit satellites that has ever existed to bring internet access to every square millimeter of the planet - Now underway and looking for Beta customers.

Class ½ Impossibilities

So it is not naiveté that brings Musk to these optimistic timelines. Rather, it's a series of successes. You look at the problem and ask yourself if engineering and innovation can achieve it, or would it require magic. If the answer is the former, then it can be accomplished. Cars will be self-driving, the only question is when. Humans will land on Mars, the question is will it be in this generation or another. When done, these will be incredible feats, but it will not be magic that brought them into existence. These accomplishments will be the product of hard-fought breakthroughs. If you have a vision, a roadmap, the ability to raise capital, the ability to attract great talent, and the ability to adapt based on feedback and learnings, the 'impossible' can be achieved. And maybe, just maybe, the people that accomplish it will be called sorcerers.

The Dunning-Kruger Effect is when someone has little skill or expertise in an area and assumes it will be easy for them. Their lack of knowledge gives them undeserved overconfidence. What Musk 'suffers' from is almost the opposite of this effect. He knows it will be Hell; it's just that he's been on the trail through Hell so many times that he could be a tour guide. And the one sure way to fail is to assume it's not achievable.

Musk has been on the trail through Hell so many times that he could be a tour guide.

What is the opposite of the Dunning-Kruger Effect? Would it be The Kruger-Dunning Effect or perhaps the Regurk-Gninnud Effect? 😃 Knowing something will be hard and doing it anyway is how great things are achieved.

The future will not be bound to a timeline. It is fickle and does not give up its secrets easily, but this should never stop the quest for a better tomorrow.

This post started off with a quote from Paul Saffo and I'll end it here with a quote from one of his contemporaries:

        "The best way to predict the future is to invent it." ~Alan Kay 

For more on Musk's Moonshot management style, check out this article.

Friday, June 12, 2020

Prius vs Model 3

The Toyota Prius was a landmark vehicle. At its introduction, it was the biggest advancement in car tech in decades. Worldwide sales of the Prius passed the 1 million milestone in May 2008, jumped the 2 million mark in September 2010, and reached 3 million in June 2013. It was selling well, it was a halo brand for Toyota and branched off many variants: Prius V, Prius C, Prius-Plug-In, and most recently Prius Prime.

Hybrid Technology Never Crossed the Chasm

Prius was the flagbearer hybrid brand in the industry. A hybrid vehicle from any manufacturer was compared to the industry benchmark, Prius. Toyota put hybrid tech into many of their other vehicles too including Lexus brands for a total of 44 different hybrid models sold around the globe.

As I write this in 2020, Toyota has sold over 15 million hybrid electric vehicles. Despite this success, hybrid vehicles have remained a niche product. Hybrid tech has a loyal following, but it has not crossed the chasm to become mainstream.

Will EVs Suffer The Same Fate?

This made me wonder if EVs would suffer the same fate of being relegated to a niche market. As one (far from conclusive) indicator, I decided to compare the sales of the flagship EV (Tesla Model 3) to sales of the flagship hybrid (Toyota Prius). 

Model 3 has been on sale for 11 quarters now, so we put the first 11 quarters of cumulative Prius sales next to the first 11 quarters of cumulative Model 3 sales. Here's that chart:


As you can see, during this time window, Model 3 is selling significantly better than Prius had. This does not guarantee that EVs will go mainstream, but it looks like the technology has a shot and, as we wrote here, and this could be the decade that it happens.

In the final quarter of Model 3 sales, Model Y was included in the date. I would have preferred to have just Model 3, but Tesla lumped Model Y and Model 3 sales together in their Q1 2020 report. Although, Model Y sales have just begun their production ramp, so their volume is not yet significant.

I thought it was important to make this comparison now, since I expect Q2 2020 numbers to be skewed by the pandemic (for Tesla, the rest of the auto industry, and most of the economy).

Will EVs go mainstream? Magic 8-Ball says 'Signs Point To Yes!'

https://en.wikipedia.org/wiki/Toyota_Prius#Sales
https://en.wikipedia.org/wiki/Tesla_Model_3#Deliveries

Thursday, June 4, 2020

2020s The Decade Of The EV


The decade* has not gotten off to a good start: a pandemic, giant killer hornets, racial strife, Ebola outbreak, Michigan dam breaches, Puerto Rico earthquakes, Australian bushfires, Cyclone Amphan, Cyclone Harold, Taal volcano eruption, Brazilian floods & mudslides...

Some of these disasters could leave an indelible mark on this decade; and while I hope that we learn our lessons from these tragedies and improve our society, that's a topic for another forum. This blog is about electric cars and EVs are sure to leave their mark on the 2020s.

Technologies frequently limp along for 10 or 20 years before the stars align and they suddenly become an "overnight success". This decade will be the one where EVs hit this overnight success tipping-point and become the norm. By the end of the decade, new car sales will be dominated by electric vehicles. When you are car shopping in 2029, considering a gas-powered car would be like considering a flip-phone in today's smartphone world.

Source: BloombergNEF
Why do I make this assertion?
  • First, EVs are more fun to drive (they are quieter, smoother, quicker) 
  • Gas prices are volatile and change with the whims of politics, saber-rattling, hurricane refinery outages... Electricity prices are far more stable and you can even generate it yourself from your own roof.
  • Battery prices have and will continue to drop. Batteries are the most expensive component in electric cars today and their price of manufacture has continued to drop. More battery factories are being built today than ever before in history.
  • EVs will be more affordable than gas cars by 2026. Today, if you consider fueling and maintenance, EVs are cheaper from the long term total cost of ownership perspective. However, for many people today, the initial sticker shock drives them away from an EV purchase. Following on the trend of battery costs, the sticker price for EVs will continue to drop. 
  • Charging speeds will increase. As battery tech improves, the causes of battery degradation will be mitigated and batteries will continue to toughen up and become tolerant to higher charging rates and more heat.
  • Ranges will increase. As battery tech improves, more energy will fit in the same space with less weight. This will be driven by both technology improvements and cost reductions.
  • Charging infrastructure will continue to proliferate. Unless you drive an EV, you are likely unaware of all of the charging infrastructure that already exists. Take a look at the map on plugshare.com, there are many places you can plug-in. And as more people start driving EVs, more infrastructure will be deployed at businesses that want to attract EV drivers and by utilities that want to sell electricity.
  • Electric fuel is cheaper. As I write this, gasoline prices are cheaper than they have been in decades. However, even at $1 per gallon, charging overnight at offpeak rates, I'm paying ~70% less per mile than a similar gas-powered car (25MPG @ $1 per gallon compared to $0.05 per kWh @ 4 miles per kWh). 
  • Update: @KennyBSAT pointed out that I forgot to mention the variety of vehicles that will become available during this decade with choices that can "carry more people or a bunch of stuff or tow, all while maintaining range." Good point!

Monday, May 25, 2020

What is Tesla's Project Dojo?


Tesla has made significant investments in artificial intelligence (AI). AI is the key to Tesla's full self-driving (FSD) future. Yet, Elon Musk has also called AI humanity's “biggest existential threat.” How do you reconcile this dichotomy? The answer is simple, Narrow AI vs General AI. A narrow AI is trained for a particular task such as playing a particular game or language processing. These narrow intelligences are not transferable. A narrow chess AI will not know anything about checkers despite the two games sharing a board. Whereas, a General AI (sometimes called Strong AI or Artificial General Intelligence(AGI)) is the hypothetical ability of a system to learn any intellectual task that a human could learn. Skills an AGI learned in one arena could be applied in new areas and an artificial superintelligence could quickly develop. An artificial superintelligence may find humans are irrelevant or worse, a threat. This is the “existential threat” that concerns Musk. 

So Tesla's FSD system will be a narrow AI, able to drive your car and you'll even be able to tell it where you'd like to go. You won't, however, be able to chat with the FSD AI about your day, but at least you'll know it won't decide that the best way to reduce traffic accidents is to kill all humans. 


Tesla's AI investments to date include creating an AI software development and validation team, creating a data labeling team, and creating an FSD hardware team to design their own custom neural network inference engine. Next on Tesla's AI investment list is "Project Dojo."


Project Dojo

We've been given a few hints about Dojo: Musk talked about it in the 2019 financial call and Tesla's Director of Artificial Intelligence and Autopilot, Andrej Karpathy, has talked about it at multiple AI conferences. We'll discuss how neural nets work and then move into some wild speculation; but first, we have to acknowledge the Dad Joke that is the name Project Dojo. We know that Project Dojo is intended to vastly improve the Autopilot Neural Network training. If you want to train, where do you go? A Dojo, of course. 



Before we get into Dojo we need to cover a few basics about neural networks. There are two fundamental phases to neural networks (NN): Training and Inference.

Training

NNs have to be trained. Training is a massive undertaking. This is when the digital ocean of data that is the training dataset must be digested. It takes terabytes of data and exaflops of compute to train a complex NN. Through training the NN forms "weights" for nodes. When the training is complete, the resulting NN is tested. A test dataset that was not part of the training dataset, where the expected results are known, is thrown at the resulting network and if the NN is properly trained, it infers the correct answer for each test. Since Project Dojo is all about training, we'll dig more into this later. Depending on the use case, there may be several stages of simulation and testing before the NN is deployed. Deploying the NN leads us to our next phase, Inference.

Inference

When a neural network receives input, it infers things about the input based on its training; this is known as “inference.” These inferences may or may not be correct. Compared to training, the storage and compute power needed for inference is significantly lower. However, in real-time applications, the inference needs to happen within milliseconds; whereas training can take hours, days, or weeks.

Unlike training, inference doesn't modify the neural network based on the results. So when the NN makes a mistake, it is important that these are captured and fed back to the training phase. This brings us to a third (optional) phase, Feedback.

Feedback

You may have heard the phrase "Data is the new Oil." Nowhere is this more applicable than AI training datasets. If you want an AI that performs well, you have to give it a training set that covers many examples of all of the types of situations that it may encounter. After you have deployed the AI, you have to collect the situations where it did the wrong thing, label it with the expected result, and add this (and perhaps hundreds or thousands of examples like it) to the training dataset. This allows the AI to iteratively improve. However, it means that your training dataset grows with each iteration and so does the amount of computing horsepower needed for training.


Tesla's Autopilot Flywheel 

Now that we've ever so briefly covered AI basics, let's look at how these apply to Tesla's FSD.

Let's start with Deploying the Neural Net. Every car that Tesla makes today is a connected car that receives over-the-air updates. This allows the cars to receive new software versions frequently. When a new version of Autopilot is deployed, Tesla collects data about its performance. The AI makes predictions such as the path of travel, where to stop, et cetera. If Autopilot is driving and you disengage it, this may be because it was doing something incorrectly. These disengagements are reported back to Tesla (assuming you have data sharing enabled). The report could be a small file that only has the data labels and a few details or it could be streams of sensor data and clips of video footage depending on the type of disengagement and the types of situations that Tesla is currently adding to their training set.

Even if Autopilot is not engaged, it is running in "shadow mode." In shadow mode, it is still making predictions and taking note when you, the human driver, don't follow those predictions. For example, if it predicts that the road bends to the left, but you go straight, this would be noted and potentially reported back to the mothership. If Autopilot infers that a traffic light is green but you stop, this data would again likely be noted and potentially reported back.

Tesla has about a million vehicles on the road today collectively driving about 15 billion miles each year. The bulk of these cars are from Tesla's Fremont factory. Tesla now has a second factory, Giga Shanghai, putting cars on the road. Soon Giga Berlin and Giga Austin (or will it be Tulsa?) will join them. All of this will result in a large amount of data for the training dataset.

The bigger the training set, the longer it takes to process. However, with a system like this, the best way to improve it is to quickly iterate (deploy it, collect errors, improve, repeat). If training takes months, this slows down the flywheel. How do you resolve this? With a supercomputer dedicated to AI training. This is Project Dojo: make a training system that can drink in the oceans of data and produce a trained NN in days instead of months.


A Cerebras Wafer Scale Engine

Cerebras

At the start, I promised some speculation. As promised, here it is.

The size of the chips used for AI training has been increasing every year. From 2013 to 2019, AI chips increased by about 50% in size. A startup called Cerebras saw this trend and extrapolated it to its natural conclusion of 1 chip per wafer. For comparison, the Cerebras chip is 56 times bigger than the largest GPU made in 2019, it has 3,000 times more on-chip memory, and it has more than 10,000 times the memory bandwidth.

This wafer-scale chip is an AI training accelerator and my conjecture is that a Cerebras chip will be at the heart of Project Dojo. This wafer-scale chip is the biggest (literally and figuratively) breakthrough in AI chip design in a long time.

There is one (albeit tenuous) thread that connects Tesla and Cerebras, both are part of ARK Invest's disruption portfolio. ARK has investments in both companies and meets with their management teams. When there are two companies that could mutually benefit working together and it would benefit their mutual investor, ARK, you can bet that introductions would be made.

Thursday, January 16, 2020

10 Years of Trading Tesla (TSLA)



Tesla's stock has been on a tear recently. I've been buying (and occasionally selling) the stock since its IPO in 2010. Below is a brief history of my trading activity.

Of course, I have no way of knowing what the stock will do tomorrow, so don't take this as stock advice.

I bought my first shares soon after the IPO. The stock opened at about $20 and had a dip over the next few weeks. In late June and early July of 2010, I bought at $18, $17.84, and (the best price I picked some up was) $16.01 per share.

I held these shares for nearly 6 years, until early 2016. Why did I sell them then? Two reasons. First, after a stock has had a good run (from $18 to $249 (or ~1400%) in this case), I like to take out my initial stake so that no matter what happens to the stock after that, I will always be net positive. The second reason I sold was that we were going to buy a new car in 2016. I didn't sell all of my shares.

My timing to sell was great. The stock dipped later in 2016 and I was able to buy the shares back at a lower price.

After taking delivery of the car in the fall of 2016, my view of the company changed. This was not my first EV (it was my 3rd actually). I knew that EVs were the future of personal transportation, but Tesla was lightyears ahead of everyone else. There was no other car that could compare. After owning a Tesla, all other cars (electric or not) seem like relics from a bygone era. They did unlock as you walked up to them, you had to push a button or turn a key to start it and stop it, they had tiny screens, they didn't have vast free Supercharging networks, they didn't have 200+ miles of range, they didn't receive firmware updates over-the-air...

Based on this two-pronged belief (1: EVs are the future. 2: Only Tesla has cracked the code), throughout 2017 and 2018, I was buying TSLA whenever the price dropped below $300. At the end of 2018, I sold a portion of my shares at $375. The reason we sold this time was once again, to buy a Tesla.

Again my sell timing was lucky. We sold near a local maximum. Soon after we sold, the SEC became concerned with Musk's infamous 420 tweet. This, and other concerns, drove the stock price down in the first half of 2019. This allowed me to buy shares back in the $200s, I even picked up some in May of 2019 for $185 per share. I had just sold for $375 and now I was able to buy it at half that price. How great is that? I understand that an investor would not be happy if they had bought at $375 and saw their investment halved. I, on the other hand, was convinced that this slump in the stock price was temporary. Issues like this get resolved and Tesla still made the best vehicles in a fast-growing category.

Now, it's early 2020 and the stock is over $500 per share. Again, I am taking some profits for the same 2 reasons I did initially. One, to remove my seed funds. Doing this allows me to sleep soundly at night. TSLA is a volatile stock. If it goes up, I still own shares and I'll share in the rewards. But if it goes down, I'm not concerned. By removing the money I initially put into it (plus a little), I am guaranteed, that (even if the stock goes to zero) I've made money on my Tesla trades. And the second reason is to again buy a Tesla product. This time we are getting Powerwalls installed on our home. More on that in later posts.

It only seems right that after making money on their stock that I should share the profits with them by buying their products. I've certainly done the same with Amazon, Netflix, and Google.

I'm still holding TSLA, I'm long the stock.

http://ts.la/patrick7819

Sunday, September 15, 2019

3 Years of Tesla Model X Ownership

In September of 2016, I bought a Tesla Model X 90D. This has been my daily driver ever since and we've taken it on multiple road trips. It has performed flawlessly. Below, we'll look at road trips, fuel costs, upgrades, and battery degradation during these years of ownership.

3-year-old (2016) Tesla Model X fresh and clean after a wash

Mileage and Road Trips

From our homebase in Portland, OR we've driven to Grants Pass; eastern Oregon; Bend, OR; San Diego, CA; Great Wolf Lodge; the dunes of Florence; Thor's Well; Crater Lake; Oregon Wildlife Safari; Cove Palisades; The Oregon Caves, and many other destinations.

On this 3rd anniversary of ownership, the X has 32,669 miles on the o-meter.

The Model X is a great vehicle for road trips. Around here, the Tesla charging network makes it easy to recharge and the stop time is just right to stretch your legs and grab a snack. Plenty of hotels have chargers, so you can start out each day with a full charge.

Fuel Cost

Can you call electricity a "fuel"? Either way, here's how much it cost us to drive these 32k miles.

About 8,000 of these miles were with free Supercharging. The bulk of the remaining miles were charged up at home in our garage. We have the time-of-use plan with our local utility and we are only charged 4.209¢ per kWh during overnight off-peak hours.

Doing a little math, we've paid $634 for 24,668 miles of travel, or 32,668 miles if you include those fueled by Superchargers. $634 for 3 years of driving is pretty good, but how does that compare to the cost we'd have paid to fuel a gas vehicle?

For the comparison, we'll look at two other Luxury Midsize SUVs from the same year: a 2016 BMW X5 M AWD 4DR and a 2016 Porsche Cayenne Turbo S. These get 14 city /19 hwy and 14/21 MPG respectively. Generously assuming the gas in the tank from the dealership covered the 668 miles, that leaves 32,000 miles worth of gas to buy.

The BWM X5 would burn about 1,940 gallons at a cost of $4,975 to travel 32k miles. The Porsche Cayenne Turbo S is only slightly better, burning 1,830 gallons of gas at a cost of $4,690. 

The Model X cost only 13.5% of the cost of the Porsche Cayenne Turbo S to fuel. That's as if we were paying 35¢ per gallon. When's the last time gasoline was 35¢ a gallon? It was around the time that Neil Armstrong walked on the Moon and that price wasn't going to last long because the OPEC oil embargo would soon follow.

As I type this, the big news story is "Two Major Saudi Oil Installations Hit by Drone Strike." Gasoline has been a problem for my entire lifetime, I think it's time to move on from this dysfunctional relationship. Electricity prices are far less volatile. No country has ever had their wind turbines as the target of a drone strike. 

Upgrades

One of the best features of any Tesla vehicle is the fact that it receives periodic software updates over-the-air. These updates add functionality and fun to the car. Here are a few of the things they've added during these 3 years:
  • Chill Mode
  • Easy Entry
  • Dog Mode 
  • Faster Supercharging
  • Battery Preconditioning 
  • New Application Launcher
  • Atari Games
  • Adaptive Suspension Damping Improvements
  • Driver Profile Key Linking
  • Heated Steering Wheel Improvements  
  • Sketchpad Improvements
  • Owners Manual Improvements
  • UI Improvements
  • Map Updates
Owners of newer Teslas might have noticed that I didn't list Navigate On Autopilot or some of the other AP related updates. That's because this was one of the last AP1 cars that Tesla made. I have no sour grapes over missing out on AP2+, I expect continuous innovation from Tesla.


Battery Degradation 

Long-time readers of this blog will know that I had a Nissan Leaf from 2011 until 2018. During those 7 years of ownership, I was greatly disappointed with the battery-life longevity. I wanted to keep the car for 10 years, but, for our needs, the battery range had degraded too much. So, battery degradation was one of my major concerns when I was EV shopping and this was one of the main reasons that I bought a Tesla. Taxi companies like Tesloop and others had bought Teslas and they were putting hundreds of thousands of miles on them each year. From their published data (see the chart below), the vehicles suffered about 10% of range loss over the first 100k miles and then the degradation flatted out and became negligible. So as long as you bought a car with ~10% more range than you needed, you should be fine.
Tesloop Battery Degradation Over 300k Miles

When new, our X had a 257-mile range. How has it held up? Today, it has a range of ~241 miles. That's a 6.3% range loss. In the first year, it lost 2.1% of range; in the second year, it lost an additional 2.2%; and in this third year, it lost 2.0%.


6.3% of Range Loss over 3 years
About the graph above, note that the left axis starts at 200 miles. This zoom-in allows you to see the degradation, but it also makes it look worse than it is. On most days, I'm only charging up to 160 miles, so the maximum range is not a significant factor.

Next year, I'm hoping to see less than 2% and for the degradation to flatten out at 230 miles. We shall see. If you plan on buying an EV and keeping it for a long time, make sure to account for some degradation as it ages.

Wrap Up

3 years of Model X ownership and I have no regrets. It was (is) the most I'd ever spent on a car. In fact, other than a house, it was the most expensive this I've ever purchased; and I'd do it again. Tesla's range, charging network, and fast charging time makes it so this can be your only vehicle. The battery management system smartly keeps the batteries from premature aging, so it should make it to the 10-year mark. I wonder what technomagical features the 2026 Tesla Model X will have.

Older Model X Reviews

You can see my 1-year review here and my 2-year review here.